Computational resources for radiomics

Radiomics has the potential to individualize patient treatment by using images that are already being routinely acquired. Defined as the extraction of quantitative imaging features from clinical images for use in statistical models, radiomics has had success in a variety of tumor sites and imaging modalities. Researchers new to the field must start by choosing software to segment tumors [or other regions of interest (ROI)], extract quantitative image features, and analyze the results. This review describes the various software programs available for these tasks and gives examples of the use of these programs in radiomics research.

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[71]  P. Marsden,et al.  False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review , 2015, PloS one.

[72]  Florent Tixier,et al.  Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III , 2016, European Journal of Nuclear Medicine and Molecular Imaging.

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